With the development of multimedia technique and network technique, a multimedia model retrieval technique is also increasingly progressing and a multimedia model retrieval system has become a key point in the research of the industry. A so-called multimedia includes texts, graphics, audio, video, three-dimensional/multidimensional models or a combination thereof. Content-based multimedia retrieval is similarity retrieval based on multimedia information features.
A conventional multimedia model retrieval method includes the following steps: Parameters of multimedia model information are read, in which the multimedia models in different formats or properties are made consistent; features of the multimedia model are extracted, in which comparable contents, i.e., features, in multimedia information are extracted and quantized; similarity degrees between models are calculated, in which a similarity degree between a model to be retrieved and a database model is calculated according to the features of the models; each group of data in the database is classified by a machine learning method in a neural network based on manual labeling of a user so as to obtain a retrieval result.
In the study of the multimedia retrieval technique, the inventor finds that there are at least following disadvantages in the above conventional multimedia model retrieval method: A multimedia information retrieval feedback system implemented in the prior art needs to label manually a plenty of models in learning and classification steps. The number of labels depends on size of the database and precision of a feature extraction method. Sometimes, thousands of data or billions of data needs to be manually labeled, thus it severely affects feedback speed. Moreover, the above method has poor applicability and robustness.